How AI Answers Local Queries — and Who Gets Cited

Local search used to be a fairly legible game: optimise your Google Business Profile, earn citations, collect reviews, and compete for the local pack. Then AI-generated answers started eating the top of the page. Now the question isn’t just can users find you — it’s will the AI mention you at all.

How AI Systems Actually Source Local Information

Semrush’s breakdown of AI information sourcing clarifies something most marketers have assumed but rarely stress-tested: AI systems draw from three overlapping pools — training data baked in before deployment, live web retrieval at query time, and licensed data partnerships. For local businesses, that third category matters more than people realise. Platforms like Yelp, TripAdvisor, and Foursquare have licensing arrangements with multiple AI providers. In Southeast Asia, equivalents like Wongnai in Thailand, Zomato across the region, and Klook for experiences sit inside similar data pipelines. If your business isn’t consistently listed — and consistently described — across these platforms, you’re invisible to systems that never visit your website at all.

The practical implication: a complete Google Business Profile is necessary but no longer sufficient. Your business description, category tags, and attributes need to be coherent across every platform that AI might pull from. Inconsistent NAP (name, address, phone) data across aggregators doesn’t just hurt traditional local pack rankings — it introduces noise that AI systems are specifically trained to distrust.

Schema Changes Are Quietly Shifting Local Visibility

The Universal Commerce Protocol’s latest spec update, reported by Search Engine Journal, adds grocery readiness signals and lays groundwork for food and lodging schema — categories that sit at the heart of local intent queries. This isn’t academic. Schema markup is one of the cleaner signals AI retrieval systems use to understand what a business is and what it offers without having to interpret unstructured page content.

For local teams in Southeast Asia, the opportunity is specific: most mid-market F&B, hospitality, and retail businesses in the region are running on templated CMS setups with zero structured data. A Vietnamese restaurant chain with 12 locations in Ho Chi Minh City that implements correct LocalBusiness schema — including opening hours, price range, accepted payment methods, and menu item markup — has a meaningful structural advantage over competitors who haven’t touched their backend since launch. The UCP schema changes signal where structured data investment is heading. Getting ahead of those spec updates, rather than reacting to them, is how proximity becomes a compounding advantage.

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GEO Is Not Just SEO with a Different Acronym

Generative Engine Optimisation requires a genuinely different editorial mindset. Where traditional SEO rewards keyword density and backlink authority, GEO rewards what Semrush’s research describes as citability: the quality of being a specific, quotable, attributable source of local knowledge. AI systems are more likely to surface businesses that have been referenced in editorial content — local news, neighbourhood blogs, regional review roundups — than those that simply have a high domain authority score.

For a Grab-heavy market like Jakarta or Kuala Lumpur, this means local PR and community content are now SEO inputs, not separate functions. A feature in a local lifestyle publication about your café’s sourcing story creates the kind of third-party citation that AI retrieval systems weight heavily. The brands that will win in AI-surfaced local search over the next 18 months are the ones building genuine local authority — not just backlink profiles — through editorial relationships, community presence, and consistent local storytelling.

The Practical Audit Your Local Team Should Run Now

Before investing in new content or campaigns, run a citation consistency audit across the platforms most likely to feed AI systems in your specific markets. In Thailand, that includes Wongnai and LINE Places. In the Philippines, it’s Zomato and local Facebook Business directories. In Indonesia, GoFood and Tokopedia merchant profiles carry weight. Check for: exact business name match, address format consistency (this trips up multi-location brands constantly), category alignment, and whether your description emphasises the neighbourhood-level signals that proximity-based AI queries reward.

On the schema side, implement or audit LocalBusiness markup at minimum. If you’re in F&B, food schema is now table stakes. If you’re in lodging, watch the UCP spec updates — the groundwork being laid now will influence how AI systems interpret accommodation inventory within 12 months. Assign someone to monitor schema spec updates the same way they monitor algorithm announcements. It’s the same game, just quieter.

Key Takeaways

• Audit citation consistency across regional platforms (Wongnai, LINE Places, Zomato, GoFood) — AI systems pull from licensing partnerships, not just Google.
• Implement LocalBusiness schema and track UCP spec updates to stay ahead of structured data requirements before they become ranking signals.
• Treat local PR and editorial mentions as GEO inputs — third-party citations in regional publications directly influence AI citability.

The local search layer is becoming the AI answer layer. The businesses that treat proximity as a passive asset — something that happens because they exist in a neighbourhood — will lose ground to those that engineer their local presence with the same rigour they apply to paid media. The question worth sitting with: if an AI were to answer ‘best [your category] near [your suburb]’ right now, what would it say — and where is it pulling that answer from?

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At grzzly, we work with Southeast Asian brands to build the kind of local search infrastructure — schema, citations, GEO-ready content — that shows up in AI-generated answers, not just traditional packs. If your local presence is coasting on an outdated playbook, we’d enjoy the conversation. Let’s talk